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首页> 外文期刊>ICES Journal of Marine Science >Non-linearity in stock–recruitment relationships of Atlantic cod: insights from a multi-model approach
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Non-linearity in stock–recruitment relationships of Atlantic cod: insights from a multi-model approach

机译：大西洋鳕鱼的股票招聘关系中的非线性：多模型方法的见解

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摘要

The stock-recruitment relationship is the basis of any stock prediction and thus fundamental for fishery management. Traditional parametric stock-recruitment models often poorly fit empirical data, nevertheless they are still the rule in fish stock assessment procedures. We here apply a multi-model approach to predict recruitment of 20 Atlantic cod (Gadus morhua) stocks as a function of adult biomass and environmental variables. We compare the traditional Ricker model with two non-parametric approaches: (i) the stochastic cusp model from catastrophe theory and (ii) multivariate simplex projections, based on attractor state-space reconstruction. We show that the performance of each model is contingent on the historical dynamics of individual stocks, and that stocks which experienced abrupt and state-dependent dynamics are best modelled using non-parametric approaches. These dynamics are pervasive in Western stocks highlighting a geographical distinction between cod stocks, which have implications for their recovery potential. Furthermore, the addition of environmental variables always improved the models' predictive power indicating that they should be considered in stock assessment and management routines. Using our multi-model approach, we demonstrate that we should be more flexible when modelling recruitment and tailor our approaches to the dynamical properties of each individual stock.
机译：股票招聘关系是任何股票预测的基础，从事渔业管理的基础。传统的参数股票招聘模型往往符合实证数据不佳，因此他们仍然是鱼类股票评估程序的规则。我们在这里采用多模型方法来预测招聘20个大西洋鳕鱼（Gadus Morhua）库存，作为成人生物量和环境变量的函数。我们将传统的Ricker模型与两种非参数方法进行比较：（i）基于吸引子状态空间重建的灾难论理论和（ii）多变量单纯x投影的随机尖端模型。我们表明每个模型的性能都取决于个体股票的历史动态，并且经历了突然和状态相关动态的库存是使用非参数方法的最佳建模。这些动态在西部股票中普遍突出了COD股之间的地理区分，这对其恢复潜力产生了影响。此外，添加环境变量总是改进了模型的预测电力，表明它们应该在股票评估和管理例程中考虑。使用我们的多模型方法，我们证明我们在建模招聘和量身定制我们对每个股票的动态特性的方法时应更加灵活。

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